featureengineering.cpp

Go to the documentation of this file.

//
// Created by Xinyu Zhang on 3/26/21.
//
#include <iostream>
#include <cosan/data/CosanData.h>
#include <cosan/base/CosanBO.h>
#include <cosan/preprocessing/overunderflow.h>
#include <cosan/preprocessing/missingvalues.h>
#include <cosan/preprocessing/normalizer.h>
#include <cosan/preprocessing/standardScaler.h>
#include <cosan/preprocessing/encoder.h>
#include <cosan/preprocessing/minmaxscaler.h>
#include <cosan/preprocessing/polynomialfeatures.h>
#include <cosan/preprocessing/principalcomponentanalysis.h>
#include <cosan/preprocessing/customtransform.h>
 
 
typedef double NumericType;
int main() {
    Cosan::CosanRawData<NumericType> CRD("./example_data/toy2/X_.csv","./example_data/toy2/Y_.csv");
    Cosan::OverUnderFlow ouf(CRD);
    Cosan::MissingValues MissV(CRD);
    Cosan::Normalizer NM(CRD,2);   
//    Cosan::StandardScaler SS(CRD);
//    Cosan::Encoder ED(CRD,true);
//    NumericType lb=0,ub=1;
//    Cosan::MinmaxScaler MMS(CRD,lb,ub);//transform X to domain [lb,ub]. If not provided, default value lb =0,ub=1
//    std::vector<std::pair<gsl::index,NumericType>> um = {{0,1.2},{1,1.1}};
//    Cosan::PolynomialFeatures PF(CRD,um,true);//um is a vector of pair where each entries indicates index and power to be generated. The third argument indicates whether the new features should be added to CRD.X. Default is false.
//    std::pair<gsl::index,gsl::index> pair = {4,5};
//    PF.SetInteractionFeatures(CRD,pair,true);//to generative interactive features from column 4 and 5.
    gsl::index ncomponent = 5;
    Cosan::PrincipalComponentAnalysis PCA(CRD,ncomponent);
//    gsl::index nrows = CRD.GetrowsX();
//    std::default_random_engine generator;
//    std::uniform_real_distribution<NumericType> distribution(0,1);
//    std::vector<NumericType> newInputX(nrows);
//    for (gsl::index  i = 0;i<nrows;i++){
//        newInputX[i] = distribution(generator);
//    }
//    Cosan::CustomTransform CT(CRD,newInputX);
 
    return 0;
}
//    
 
    // Cosan::CosanLinearRegression CLR(true);
//     CLR.fit(CD.GetInput(),CD.GetTarget());
 
    // std::cout<<CD.GetrowsX()<<" "<<CD.GetcolsX()<<" "<<std::endl;
    // for (auto each : CD.GetIdxpinfX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
    // for (auto each : CD.GetIdxminfX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
    // for (auto each : CD.GetIdxmissingX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
    // for (auto each : CD.GetcolCatX()) {std::cout<<each<<std::endl;}
    // for (auto each : CD.GetsvaluesX()) {std::cout<<each<<std::endl;}
    // for (const auto & [ key, value ] : CD.GetRawToNumIdx()){
    //  std::cout<<key<<":"<<value<<std::endl;}
    // for (const auto & [ key, value ] : CD.GetRawToCatIdx()){
    //  std::cout<<key<<":"<<value<<std::endl;}     
 
//     Cosan::CosanRawData CD1("./example_data/toy2/X_2.csv");
 
//     // Cosan::CosanLinearRegression CLR(true);
// //     CLR.fit(CD.GetInput(),CD.GetTarget());
//     std::cout<<CD1.GetSummaryMessageX()<<std::endl;
//     // std::cout<<CD.GetrowsX()<<" "<<CD.GetcolsX()<<" "<<std::endl;
//     // for (auto each : CD.GetIdxpinfX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
//     // for (auto each : CD.GetIdxminfX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
//     // for (auto each : CD.GetIdxmissingX()) {std::cout<<each[0]<<" "<<each[1]<<std::endl;}
//     // for (auto each : CD.GetcolCatX()) {std::cout<<each<<std::endl;}
//     // for (auto each : CD.GetsvaluesX()) {std::cout<<each<<std::endl;}
//     for (const auto & [ key, value ] : CD1.GetRawToNumIdx()){
//      std::cout<<key<<":"<<value<<std::endl;}
//     for (const auto & [ key, value ] : CD1.GetRawToCatIdx()){
//      std::cout<<key<<":"<<value<<std::endl;}     
 
    // std::cout<<CD.GetInput()<<std::endl;
 
//    double RegularizationTerm = 1;
//    Cosan::CosanRidgeRegression CRR(RegularizationTerm,true);
//    CRR.fit(CD.GetInput(),CD.GetTarget());
//    std::cout<<CRR.GetBeta()<<std::endl;
//    save_csv("./example_data/toy/beta_c1.csv",CRR.GetBeta());
 
//    m(0, 0) = 3;
//    m(1, 0) = 2.5;
//    m(0, 1) = -1;
//    m(1,1)  = m(1,0)+m(0,1);
//    std::cout<<y.rows()<<y.cols()<<std::endl;
    // Eigen::MatrixXd beta = (X.transpose()*X).ldlt().solve(X.transpose()*y);
    
    
//
//    m.transposeInPlace();
//    cout<<m<<endl;
//    m.resize(1,4);
//    cout<<n<<endl;